DANN: a deep learning approach for annotating the pathogenicity of genetic variants
DANN: a deep learning approach for annotating the pathogenicity of genetic variants
复制标题
DOI：
10.1093/bioinformatics/btu703
复制
发表时间：
2015-03-01
期刊：
影响因子：
5.8
通讯作者：
Xie, Xiaohui
中科院分区：
文献类型：
--
作者：
Quang, Daniel;Chen, Yifei;Xie, Xiaohui
The Summary: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD's SVM methodology.
